import os
from llama_index.llms.openai import OpenAI
from llama_index.core.query_engine import CitationQueryEngine
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
from llama_index.core import SimpleDirectoryReader
from llama_index.core.node_parser import SimpleNodeParser
from llama_index.core import  GPTVectorStoreIndex,VectorStoreIndex
from llama_index.llms import openai_like
from llama_index.core import Settings
from llama_index.llms.ollama import Ollama
from llama_index.embeddings.huggingface import HuggingFaceEmbedding  # HuggingFaceEmbedding:用于将文本转换为词向量
from llama_index.llms.huggingface import HuggingFaceLLM  # HuggingFaceLLM：用于运行Hugging Face的预训练语言模型
from llama_index.core import Settings,SimpleDirectoryReader,VectorStoreIndex
import chromadb
from llama_index.embeddings.dashscope import DashScopeEmbedding
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext, load_index_from_storage
from llama_index.llms.deepseek  import DeepSeek
from llama_index.embeddings.fastembed import FastEmbedEmbedding
    # 连接Chroma数据库


llm = DeepSeek(model="deepseek-chat", api_key="sk-605e60a1301040759a821b6b677556fb")
Settings.llm = llm
embed_model = FastEmbedEmbedding(model_name="BAAI/bge-small-en-v1.5")
Settings.embed_model = embed_model


from llama_index.core import (
    VectorStoreIndex,
    SimpleDirectoryReader,
    StorageContext,
    load_index_from_storage,
)
if not os.path.exists("./citation"):
    documents = SimpleDirectoryReader("./data/paul_graham").load_data()
    index = VectorStoreIndex.from_documents(
        documents,
    )
    index.storage_context.persist(persist_dir="./citation")
else:
    index = load_index_from_storage(
        StorageContext.from_defaults(persist_dir="./citation"),
    )

query_engine = CitationQueryEngine.from_args(
    index,
    similarity_top_k=3,
    # here we can control how granular citation sources are, the default is 512
    citation_chunk_size=512,
)

response = query_engine.query("What did the author do growing up?")

print(response)

print(response.source_nodes[0].node.get_text())